CA2884471A1 - Generation of comfort noise - Google Patents
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- G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
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Abstract
A comfort noise controller (50) for generating CN (Comfort Noise) control parameters is described. A buffer (200) of a predetermined size is configured to store CN parameters for SID (Silence Insertion Descriptor) frames and active hangover frames. A subset selector (50A) is configured to determine a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on residual energies. A comfort noise control parameter extractor (50B) is configured to use the determined CN parameter subset to determine the CN control parameters for a first SID frame following an active signal frame.
Description
GENERATION OF COMFORT NOISE
TECHNICAL FIELD
The proposed technology generally relates to generation of comfort noise (CN), and particularly to generation of comfort noise control parameters.
BACKGROUND
In coding systems used for conversational speech it is common to use dis-continuous transmission (DTX) to increase the efficiency of the encoding.
This is motivated by large amounts of pauses embedded in the conversation-al speech, e.g. while one person is talking the other one is listening. By using DTX the speech encoder can be active only about 50 percent of the time on average. Examples of codecs that have this feature are the 3GPP Adaptive Multi-Rate Narrowband (AMR NB) codec and the ITU-T G.718 codec.
In DTX operation active frames are coded in the normal codec modes, while inactive signal periods between active regions are represented with comfort noise. Signal describing parameters are extracted and encoded in the encod-er and transmitted to the decoder in silence insertion description (SID) frames. The SID frames are transmitted at a reduced frame rate and a lower bit rate than used for the active speech coding mode(s). Between the SID
frames no information about the signal characteristics is transmitted. Due to the low SID rate the comfort noise can only represent relatively stationary properties compared to the active signal frame coding. In the decoder the re-ceived parameters are decoded and used to characterize the comfort noise.
For high quality DTX operation, i.e. without degraded speech quality, it is important to detect the periods of speech in the input signal. This is done by using a voice activity detector (VAD) or a sound activity detector (SAD). Fig.
shows a block diagram of a generalized VAD, which analyses the input signal in data frames (of 5-30 ms depending on the implementation), and produces an activity decision for each frame.
A preliminary activity decision (Primary VAD Decision) is made in a primary voice detector 12 by comparison of features for the current frame estimated by a feature extractor 10 and background features estimated from previous input frames by a background estimation block 14. A difference larger than a specified threshold causes the active primary decision. In a hangover addi-tion block 16 the primary decision is extended on the basis of past primary decisions to form the final activity decision (Final VAD Decision). The main reason for using hangover is to reduce the risk of mid and backend clipping in speech segments.
For speech codecs based on linear prediction (LP), e.g. G.718, it is reasona-1 5 ble to model the envelope and frame energy using a similar representation as for the active frames. This is beneficial since the memory requirements and complexity for the codec can be reduced by common functionality between the different modes in DTX operation.
For such codecs the comfort noise can be represented by its LP coefficients (also known as auto regressive (AR) coefficients) and the energy of the LP re-sidual, i.e. the signal that as input to the LP model gives the reference audio segment. In the decoder, a residual signal is generated in the excitation gen-erator as random noise which gets shaped by the CN parameters to form the comfort noise.
The LP coefficients are typically obtained by computing the autocorrelations did of the windowed audio segments x[n] , n = 0 ,. . .,N -1 in accordance with:
r[k] =Ex[n]x[n - k], k = 0 , . . . , P (1) n=k where P is the pre-defined model order. Then the LP coefficients a k are ob-tained from the autocorrelation sequence using e.g. the Levinson-Durbin al-gorithm.
In a communication system where such a codec is utilized, the LP coeffi-cients should be efficiently transmitted from the encoder to the decoder. For this reason more compact representations that may be less sensitive to quantization noise are commonly used. For example, the LP coefficients can be transformed into linear spectral pairs (LSP). In alternative implementa-1 0 tions the LP coefficients may instead be converted to the immitance spec-trum pairs (ISP), line spectrum frequencies (LSF) or immitance spectrum fre-quencies (ISF) domains.
The LP residual is obtained by filtering the reference signal through an in-verse LP synthesis filter A[z] defined by:
P
A[z] =1 + Ea kz- k (2) k=1 The filtered residual signal s[n] is consequently given by:
P
s[n]= x[n] +Ea kx[n - k], n = 0 ,..., N - 1 (3) k=1 for which the energy is defined as:
E = ¨Es[n]2 (4) Due to the low transmission rate of SID frames, the CN parameters should evolve slowly in order to not change the noise characteristics rapidly. For ex-ample, the G.718 codec limits the energy change between SID frames and interpolates the LSP coefficients to handle this.
To find representative CN parameters at the SID frames, LSP coefficients and residual energy are computed for every frame, including no data frames (thus, for no data frames the mentioned parameters are determined but not transmitted). At the SID frame the median LSP coefficients and mean residu-al energy are computed, encoded and transmitted to the decoder. In order for the comfort noise to not be unnaturally static, random variations may be added to the comfort noise parameters, e.g. a variation of the residual ener-gy. This technique is for example used in the G.718 codec.
In addition, the comfort noise characteristics are not always well matched to the reference background noise, and slight attenuation of the comfort noise may reduce the listener's attention to this. The perceived audio quality can consequently become higher. In addition, the coded noise in active signal frames might have lower energy than the uncoded reference noise. Therefore attenuation may also be desirable for better energy matching of the noise representation in active and inactive frames. The attenuation is typically in the range 0 - 5dB, and can be fixed or dependent on the active coding mode(s) bitrates.
In high efficient DTX systems a more aggressive VAD might be used and high energy parts of the signal (relative to the background noise level) can accord-ingly be represented by comfort noise. In that case, limiting the energy change between the SID frames would cause perceptual degradation. To bet-ter handle the high energy segments, the system may allow larger instant changes of CN parameters for these circumstances.
Low-pass filtering or interpolation of the CN parameters is performed at the inactive frames in order to get natural smooth comfort noise dynamics. For the first SID frame following one or several active frames (from now on just denoted the "first SID"), the best basis for LSP interpolation and energy smoothing would be the CN parameters from previous inactive frames, i.e.
prior to the active signal segment.
For each inactive frame, SID or no data, the LSP vector qi can be interpolat-5 ed from previous LSP coefficients according to:
q, - a iirsm + (1 a ) qi-]
(5) where i is the frame number of inactive frames, a e [0,1] is the smoothing factor and .SID are the median LSP coefficients computed with parameters from current SID and all no data frames since the previous SID frame. For the G.718 codec a smoothing factor a =0.1 is used.
The residual energy Et is similarly interpolated at the SID or no data frames according to:
= E SID + (1 (6) where p c [0,1] is the smoothing factor and r SID is the averaged energy for current SID and no data frames since the previous SID frame. For the G.718 codec a smoothing factor f3 = 0.3 is used.
An issue with the described interpolation is that for the first SID the interpo-lation memories (E, 1 and qi 1 ) may relate to previous high energy frames, e.g. unvoiced speech frames, which are classified as inactive by the VAD. In that case the first SID interpolation would start from noise characteristics that are not representative for the coded noise in the close active mode hangover frames. The same issue occurs if the characteristics of the back-ground noise are changed during active signal segments, e.g. segments of a speech signal.
An example of the problems related to prior art technologies is shown in Fig.
2. The spectrogram of a noisy speech signal encoded in DTX operation shows two segments of comfort noise before and after a segment of active coded au-dio (such as speech). It can be seen that when the noise characteristics from the first CN segment are used for the interpolation in the first SID, there is an abrupt change of the noise characteristics. After some time the comfort noise matches the end of the active coded audio better, but the bad transi-tion causes a clear degradation of the perceived audio quality.
Using higher smoothing factors a and f3 would focus the CN parameters to the characteristics of the current SID, but this could still cause problems.
Since the parameters in the first SID cannot be averaged during a period of noise, as following SID frames can, the CN parameters are only based on the signal properties in the current frame. Those parameters might represent the background noise at the current frame better than the long term characteris-tic in the interpolation memories. It is however possible that these SID pa-rameters are outliers, and do not represent the long term noise characteris-tics. That would for example result in rapid unnatural changes of the noise characteristics, and a lower perceived audio quality.
SUMMARY
An object of the proposed technology is to overcome at least one of the above stated problems.
A first aspect of the proposed technology involves a method of generating CN
control parameters. The method includes the following steps:
= Storing CN parameters for SID frames and active hangover frames in a buffer of a predetermined size.
TECHNICAL FIELD
The proposed technology generally relates to generation of comfort noise (CN), and particularly to generation of comfort noise control parameters.
BACKGROUND
In coding systems used for conversational speech it is common to use dis-continuous transmission (DTX) to increase the efficiency of the encoding.
This is motivated by large amounts of pauses embedded in the conversation-al speech, e.g. while one person is talking the other one is listening. By using DTX the speech encoder can be active only about 50 percent of the time on average. Examples of codecs that have this feature are the 3GPP Adaptive Multi-Rate Narrowband (AMR NB) codec and the ITU-T G.718 codec.
In DTX operation active frames are coded in the normal codec modes, while inactive signal periods between active regions are represented with comfort noise. Signal describing parameters are extracted and encoded in the encod-er and transmitted to the decoder in silence insertion description (SID) frames. The SID frames are transmitted at a reduced frame rate and a lower bit rate than used for the active speech coding mode(s). Between the SID
frames no information about the signal characteristics is transmitted. Due to the low SID rate the comfort noise can only represent relatively stationary properties compared to the active signal frame coding. In the decoder the re-ceived parameters are decoded and used to characterize the comfort noise.
For high quality DTX operation, i.e. without degraded speech quality, it is important to detect the periods of speech in the input signal. This is done by using a voice activity detector (VAD) or a sound activity detector (SAD). Fig.
shows a block diagram of a generalized VAD, which analyses the input signal in data frames (of 5-30 ms depending on the implementation), and produces an activity decision for each frame.
A preliminary activity decision (Primary VAD Decision) is made in a primary voice detector 12 by comparison of features for the current frame estimated by a feature extractor 10 and background features estimated from previous input frames by a background estimation block 14. A difference larger than a specified threshold causes the active primary decision. In a hangover addi-tion block 16 the primary decision is extended on the basis of past primary decisions to form the final activity decision (Final VAD Decision). The main reason for using hangover is to reduce the risk of mid and backend clipping in speech segments.
For speech codecs based on linear prediction (LP), e.g. G.718, it is reasona-1 5 ble to model the envelope and frame energy using a similar representation as for the active frames. This is beneficial since the memory requirements and complexity for the codec can be reduced by common functionality between the different modes in DTX operation.
For such codecs the comfort noise can be represented by its LP coefficients (also known as auto regressive (AR) coefficients) and the energy of the LP re-sidual, i.e. the signal that as input to the LP model gives the reference audio segment. In the decoder, a residual signal is generated in the excitation gen-erator as random noise which gets shaped by the CN parameters to form the comfort noise.
The LP coefficients are typically obtained by computing the autocorrelations did of the windowed audio segments x[n] , n = 0 ,. . .,N -1 in accordance with:
r[k] =Ex[n]x[n - k], k = 0 , . . . , P (1) n=k where P is the pre-defined model order. Then the LP coefficients a k are ob-tained from the autocorrelation sequence using e.g. the Levinson-Durbin al-gorithm.
In a communication system where such a codec is utilized, the LP coeffi-cients should be efficiently transmitted from the encoder to the decoder. For this reason more compact representations that may be less sensitive to quantization noise are commonly used. For example, the LP coefficients can be transformed into linear spectral pairs (LSP). In alternative implementa-1 0 tions the LP coefficients may instead be converted to the immitance spec-trum pairs (ISP), line spectrum frequencies (LSF) or immitance spectrum fre-quencies (ISF) domains.
The LP residual is obtained by filtering the reference signal through an in-verse LP synthesis filter A[z] defined by:
P
A[z] =1 + Ea kz- k (2) k=1 The filtered residual signal s[n] is consequently given by:
P
s[n]= x[n] +Ea kx[n - k], n = 0 ,..., N - 1 (3) k=1 for which the energy is defined as:
E = ¨Es[n]2 (4) Due to the low transmission rate of SID frames, the CN parameters should evolve slowly in order to not change the noise characteristics rapidly. For ex-ample, the G.718 codec limits the energy change between SID frames and interpolates the LSP coefficients to handle this.
To find representative CN parameters at the SID frames, LSP coefficients and residual energy are computed for every frame, including no data frames (thus, for no data frames the mentioned parameters are determined but not transmitted). At the SID frame the median LSP coefficients and mean residu-al energy are computed, encoded and transmitted to the decoder. In order for the comfort noise to not be unnaturally static, random variations may be added to the comfort noise parameters, e.g. a variation of the residual ener-gy. This technique is for example used in the G.718 codec.
In addition, the comfort noise characteristics are not always well matched to the reference background noise, and slight attenuation of the comfort noise may reduce the listener's attention to this. The perceived audio quality can consequently become higher. In addition, the coded noise in active signal frames might have lower energy than the uncoded reference noise. Therefore attenuation may also be desirable for better energy matching of the noise representation in active and inactive frames. The attenuation is typically in the range 0 - 5dB, and can be fixed or dependent on the active coding mode(s) bitrates.
In high efficient DTX systems a more aggressive VAD might be used and high energy parts of the signal (relative to the background noise level) can accord-ingly be represented by comfort noise. In that case, limiting the energy change between the SID frames would cause perceptual degradation. To bet-ter handle the high energy segments, the system may allow larger instant changes of CN parameters for these circumstances.
Low-pass filtering or interpolation of the CN parameters is performed at the inactive frames in order to get natural smooth comfort noise dynamics. For the first SID frame following one or several active frames (from now on just denoted the "first SID"), the best basis for LSP interpolation and energy smoothing would be the CN parameters from previous inactive frames, i.e.
prior to the active signal segment.
For each inactive frame, SID or no data, the LSP vector qi can be interpolat-5 ed from previous LSP coefficients according to:
q, - a iirsm + (1 a ) qi-]
(5) where i is the frame number of inactive frames, a e [0,1] is the smoothing factor and .SID are the median LSP coefficients computed with parameters from current SID and all no data frames since the previous SID frame. For the G.718 codec a smoothing factor a =0.1 is used.
The residual energy Et is similarly interpolated at the SID or no data frames according to:
= E SID + (1 (6) where p c [0,1] is the smoothing factor and r SID is the averaged energy for current SID and no data frames since the previous SID frame. For the G.718 codec a smoothing factor f3 = 0.3 is used.
An issue with the described interpolation is that for the first SID the interpo-lation memories (E, 1 and qi 1 ) may relate to previous high energy frames, e.g. unvoiced speech frames, which are classified as inactive by the VAD. In that case the first SID interpolation would start from noise characteristics that are not representative for the coded noise in the close active mode hangover frames. The same issue occurs if the characteristics of the back-ground noise are changed during active signal segments, e.g. segments of a speech signal.
An example of the problems related to prior art technologies is shown in Fig.
2. The spectrogram of a noisy speech signal encoded in DTX operation shows two segments of comfort noise before and after a segment of active coded au-dio (such as speech). It can be seen that when the noise characteristics from the first CN segment are used for the interpolation in the first SID, there is an abrupt change of the noise characteristics. After some time the comfort noise matches the end of the active coded audio better, but the bad transi-tion causes a clear degradation of the perceived audio quality.
Using higher smoothing factors a and f3 would focus the CN parameters to the characteristics of the current SID, but this could still cause problems.
Since the parameters in the first SID cannot be averaged during a period of noise, as following SID frames can, the CN parameters are only based on the signal properties in the current frame. Those parameters might represent the background noise at the current frame better than the long term characteris-tic in the interpolation memories. It is however possible that these SID pa-rameters are outliers, and do not represent the long term noise characteris-tics. That would for example result in rapid unnatural changes of the noise characteristics, and a lower perceived audio quality.
SUMMARY
An object of the proposed technology is to overcome at least one of the above stated problems.
A first aspect of the proposed technology involves a method of generating CN
control parameters. The method includes the following steps:
= Storing CN parameters for SID frames and active hangover frames in a buffer of a predetermined size.
= Determining a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on residual energies.
= Using the determined CN parameter subset to determine the CN control parameters for a first SID frame following an active signal frame.
A second aspect of the proposed technology involves a computer program for generating CN control parameters. The computer program comprises computer readable code units which when run on a computer causes the computer to:
= Store CN parameters for SID frames and active hangover frames in a buffer of a predetermined size.
= Determine a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on residual energies.
= Use the determined CN parameter subset to determine the CN control pa-rameters for a first SID frame ("First SID") following an active signal frame.
A third aspect of the proposed technology involves a computer program prod-uct, comprising computer readable medium and a computer program ac-cording to the second aspect stored on the computer readable medium.
A fourth aspect of the proposed technology involves a comfort noise controller for generating CN control parameters. The apparatus includes:
= A buffer of a predetermined size configured to store CN parameters for SID
frames and active hangover frames.
= A subset selector configured to determine a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on resid-ual energies.
= Using the determined CN parameter subset to determine the CN control parameters for a first SID frame following an active signal frame.
A second aspect of the proposed technology involves a computer program for generating CN control parameters. The computer program comprises computer readable code units which when run on a computer causes the computer to:
= Store CN parameters for SID frames and active hangover frames in a buffer of a predetermined size.
= Determine a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on residual energies.
= Use the determined CN parameter subset to determine the CN control pa-rameters for a first SID frame ("First SID") following an active signal frame.
A third aspect of the proposed technology involves a computer program prod-uct, comprising computer readable medium and a computer program ac-cording to the second aspect stored on the computer readable medium.
A fourth aspect of the proposed technology involves a comfort noise controller for generating CN control parameters. The apparatus includes:
= A buffer of a predetermined size configured to store CN parameters for SID
frames and active hangover frames.
= A subset selector configured to determine a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on resid-ual energies.
= A comfort noise control parameter extractor configured to use the deter-mined CN parameter subset to determine the CN control parameters for a first SID frame following an active signal frame.
A fifth aspect of the proposed technology involves a decoder including a com-fort noise controller in accordance with the fourth aspect.
A sixth aspect of the proposed technology involves a network node including a decoder in accordance with the fifth aspect.
A seventh aspect of the proposed technology involves a network node including a comfort noise controller in accordance with the fourth aspect.
An advantage of the proposed technology is that it improves the audio quali-1 5 ty for switching between active and inactive coding modes for codecs operat-ing in DTX mode. The envelope and signal energy of the comfort noise are matched to previous signal characteristics of similar energies in previous SID
and VAD hangover frames.
BRIEF DESCRIPTION OF THE DRAWINGS
The proposed technology, together with further objects and advantages thereof, may best be understood by making reference to the following description taken together with the accompanying drawings, in which:
Fig. 1 is a block diagram of a generic VAD;
Fig. 2 is an example of a spectrogram of a noisy speech signal that has been decoded in accordance with prior art DTX solutions;
Fig. 3 is a block diagram of an encoder system in a codec;
Fig. 4 is a block diagram of an example embodiment of a decoder imple-3 0 menting the method of generating comfort noise according the proposed tech-nology;
Fig. 5 is an example of a spectrogram of a noisy speech signal that has been decoded in accordance with the proposed technology;
A fifth aspect of the proposed technology involves a decoder including a com-fort noise controller in accordance with the fourth aspect.
A sixth aspect of the proposed technology involves a network node including a decoder in accordance with the fifth aspect.
A seventh aspect of the proposed technology involves a network node including a comfort noise controller in accordance with the fourth aspect.
An advantage of the proposed technology is that it improves the audio quali-1 5 ty for switching between active and inactive coding modes for codecs operat-ing in DTX mode. The envelope and signal energy of the comfort noise are matched to previous signal characteristics of similar energies in previous SID
and VAD hangover frames.
BRIEF DESCRIPTION OF THE DRAWINGS
The proposed technology, together with further objects and advantages thereof, may best be understood by making reference to the following description taken together with the accompanying drawings, in which:
Fig. 1 is a block diagram of a generic VAD;
Fig. 2 is an example of a spectrogram of a noisy speech signal that has been decoded in accordance with prior art DTX solutions;
Fig. 3 is a block diagram of an encoder system in a codec;
Fig. 4 is a block diagram of an example embodiment of a decoder imple-3 0 menting the method of generating comfort noise according the proposed tech-nology;
Fig. 5 is an example of a spectrogram of a noisy speech signal that has been decoded in accordance with the proposed technology;
Fig. 6 is a flow chart illustrating an example embodiment of the method in accordance with the proposed technology;
Fig. 7 is a flow chart illustrating another example embodiment of the method in accordance with the proposed technology;
Fig. 8 is a block diagram illustrating an example embodiment of the com-fort noise controller in accordance with the proposed technology;
Fig. 9 is a block diagram illustrating another example embodiment of the comfort noise controller in accordance with the proposed technology;
Fig. 10 is a block diagram illustrating another example embodiment of the comfort noise controller in accordance with the proposed technology;
Fig. 11 is a schematic diagram showing some components of an example embodiment of a decoder, wherein the functionality of the decoder is imple-mented by a computer; and Fig. 12 is a block diagram illustrating a network node that includes a comfort noise controller in accordance with the proposed technology.
DETAILED DESCRIPTION
The embodiments described below relate to a system of audio encoder and decoder mainly intended for speech communication applications using DTX
with comfort noise for inactive signal representation. The system that is con-sidered utilizes LP for coding of both active and inactive signal frames, where a VAD is used for activity decisions.
In the encoder illustrated in Fig. 3 a VAD 18 outputs an activity decision which is used for the encoding by an encoder 20. In addition, the VAD hang-over decision is put into the bitstream by a bitstream multiplexer (MUX) 22 and transmitted to the decoder together with the coded parameters of active frames (hangover and non-hangover frames) and SID frames.
The disclosed embodiments are part of an audio decoder. Such a decoder 100 is schematically illustrated in figure 4. A bitstream demultiplexer (DEMUX) 24 demultiplexes the received bitstream into coded parameters and VAD hangover decisions. The demultiplexed signals are forwarded to a mode selector 26. Received coded parameters are decoded in a parameter decoder 28. The decoded parameters are used by an active frame decoder 30 to de-code active frames from the mode selector 26.
The decoder 100 also includes a buffer 200 of a predetermined size M and configured to receive and store CN parameters for SID and active mode hangover frames, a unit 300 configured to determine which of the stored CN
parameters that are relevant for SID based on the age of stored CN parame-1 0 ters, a unit 400 configured to determine which of the determined CN
param-eters that are relevant for SID based on residual energy measurements, and a unit 500 configured to use the determined CN parameters that are relevant for SID for the first SID frame following active signal frame(s).
The parameters in the buffers are constrained to be recent in order to be rel-evant. Thereby the sizes of the buffers used for selection of relevant buffer subsets are reduced during longer periods of active coding. Additionally the stored parameters are replaced by newer values during SID and actively cod-ed hangover frames.
By using circular buffers the complexity and memory requirement for the buffer handling can be reduced. In such implementation the already stored elements do not have to be moved when a new element is added. The posi-tion of the last added parameter, or parameter set, is used together with the size of the buffer to place new elements. When new elements are added, old elements might be overwritten.
Since the buffers hold parameters from earlier SID and hangover frames they describe signal characteristics of previous audio frames that probably, but not necessarily, contain background noise. The number of parameters that are considered relevant is defined by the size of the buffer and the time, or corresponding number of frames, elapsed since the information was stored.
The technology disclosed herein can be described in a number of algorithmic steps, e.g. performed at the decoder side illustrated in Fig. 4. These steps are:
la. Step la (performed by the unit denoted step la in Fig. 4) - Buffer update for SID and hangover frames:
For each SID and active hangover frame the quantized LSP coefficient vector 4 and corresponding quantized residual energy E are stored (in buffer 200) in buffers Qm = qmõ ,} and Em = Eom,...,Emm 1} , i.e.
qmj (7) Em =
The buffer position index j e [ 0,M -1] is increased by one prior to each buffer update and reset if the index exceeds the buffer size M, i.e.
j =0 f j > M -1 (8) As will be described below, subsets QK and EK of the K0 latest stored elements in QM and EM, respectively, define the sets of stored parame-ters.
lb. Step lb (performed by the unit denoted step lb in Fig. 4) - Buffer update for active non-hangover frames During decoding of active frames, the size of subsets QK and EK is de-creased by a rate of y' elements per frame according to:
{K = K
0 if pA<Y
(9) K = K -1 for 17 = y pA<(ri +1) = y where K0 is the number of stored elements in previous SID and hango-ver frames, ri cZ and PA is the number of consecutive active non-hangover frames. The rate of decrement relates to time, where y =25 is feasible for 20 ms frames. This corresponds to a decrease by one ele-ment every half second while decoding active frames. The decrement rate constant y can potentially be defined as any value y c Z , but it should be chosen such that old noise characteristics that are likely not to represent the current background noise are excluded from the sub-sets QK and EK . The value might for example be chosen based on the expected dynamics of the background noise. In addition, the natural length of speech bursts and the behavior of the VAD may be considered, as long sequences of consecutive active frames are unlikely. Typically the constant would be in the range y 500 for 20 ms frames, which cor-responds to less than 10 seconds. As an alternative equation (9) may be written in a more compact form as:
K = K0-17 for 17 = y pA <(17 +1) = y (10) where K0 is the number of CN parameters for SID frames and active hango-ver frames stored in the buffer 200, y is a predetermined constant, ri is a non-negative integer.
2. Step 2 (perfor _________________________________________________________________ tried by the unit denoted step 2 in Fig. 4) - Selection of relevant buffer elements At the first SID following active frames a subset of the buffer EK is se-lected based on the residual energies. The subset EN = { Eos ,...,E.. i } EK
of size L is defined as:
Es {EkK c EKE ri <EkK <EkKo +12}
for k =k0,...,kK 1 (11) where EkKo is the latest stored residual energy, yi and 12 are predetermined lower and upper bounds, respec-tively, for residual energies considered to be representative of noise at a transition from active to inactive frames (for example yi = 200 and y2 = 20 ), ko,...,k, I are sorted such that ko corresponds to the latest and ki, 1 to the oldest stored CN parameter.
Typically, 12 is selected from the range 12 c [0,100] as larger values would include high residual energies compared to the latest stored residual energy EkKo . This could cause a significant step-up of the comfort noise energy that would cause an audible degradation. It is also desirable to exclude signal characteristics from speech frames, which generally have larger energy, as these characteristics are generally not representing the background noise well. 11 can be selected slightly larger than 12' e.g.
from the range 11 c [50,500], as a step-down in energy is usually less an-noying. Additionally, the likelihood of including speech signal character-istics is generally less for frames with a residual energy less than EkKo than it is for frames with a residual energy larger than EkKo .
It should be noted that the energies EkK can as well as in linear domain be represented in a logarithmic domain, e.g. dB. With energies in loga-rithmic domain the selection of relevant buffer elements, as specified in equation (11), is described equivalently with energies EkK in linear do-main as:
Es = EkK c EKE <EK < EkKo 172. } for k 1 (12) where log(Ti )= -yi and 1og(1-2) = 12. Suitable boundaries specifying the subset of the buffer EK are for example given by Ti = 0.7 and 22 = 1.03 or c [0.5,0.9] and 12 E [1.0,1.25] .
The corresponding vectors in the LSP buffer QK define the subset Qs-rs 3. Step 3 (performed by the unit denoted step 3 in Fig. 4) - Determination of representative comfort noise parameters To find a representative residual energy the weighted mean of the sub-set Es is computed as:
Ewks Eks E _ ______________________________________________________________________________ (13) k=0 where w: are the elements in the subset of weights:
s wm E wm w = for VjlE7 e For a maximum buffer size AI = 8 a suitable set of weights is:
wm = {0.2, 0.16, 0.128, 0.1024, 0.08192, 0.065536, 0.0524288, 0.01048576}
This means that recent energies get more weight in the residual energy mean E, which makes the energy transition between active and inactive frames smoother.
Among LSP vectors in the subset QS, the median LSP vector is selected by computing the distances between all the LSP vectors in the subset buffer Es according to:
P
Rim E(q, is [p] qms [42 for l,m=0,...,L -1 ( 14) P=1 where q[p] are the elements in the vector qis .
For every LSP vector the distance to the other vectors are summed, i.e.
Si = E Rim for l =0,...,L -1 (15) m=0 The median LSP vector is given by the vector with the smallest distance to the other vectors in the subset buffer, i.e.
el = { cli E Qs 1 si sõõ t # m } for l,m =0,...,L -1 (16) If several vectors have equal total distance, the median can be arbitrari-ly chosen among those vectors.
As an alternative representative LSP vector may be determined as the mean vector of the subset Qs .
4. Step 4 (performed by the unit denoted step 4 in Fig. 4) - Interpolation of comfort noise parameters for first SID frame The LSP median or mean vector 44 and the averaged residual energy E
are used in the interpolation of CN parameters in the first SID frame as described in equation (5) and (6) with:
(17) E1-1 = E
The values of iva .SID and E's, are obtained from the parameter decoder 28.
The smoothing factors a e [0,1] and p c [0,1] can for the first SID frame be different from the factors used in following SID and no data frames interpolation of CN parameters. Additionally, the factors could for ex-ample be dependent on a measure that further describe the reliability of the determined parameters el and E, e.g. the size of the subsets Qs and Es. Suitable values are for example a =0.2 and f3 = 0.2 or f3 = 0.05.
The comfort noise parameters for the first SID frame are then used by a comfort noise generator 32 to control filling of no data frames from mode selector 26 with noise based on excitations from excitation gener-ator 34.
If the subsets Qs and Es are empty, the latest extracted SID parameters may be used directly without interpolation from older noise parameters.
The transmitted LSP vector .SID used in the interpolation is in the encoder usually obtained directly from the LP analysis of the current frame, i.e. no previous frames are considered. The transmitted residual energy r SID is pref-erably obtained using LP parameters corresponding to the LSP parameters used for the signal synthesis in the decoder. These LSP parameters can be obtained in the encoder by performing steps 1-4 with a corresponding en-coder side buffer. Operating the encoder in this way implies that the energy of the decoder output can be matched to the input signal energy by control of the encoded and transmitted residual energy since the decoder synthesis LP parameters are known in the encoder.
Fig. 5 is an example of a spectrogram of a noisy speech signal that has been de-coded in accordance with the proposed technology. The spectrogram corre-sponds to the spectrogram in Fig. 2, i.e. it is based on the same encoder side input signal. By comparing the spectrograms of the prior art (Fig. 2) and the proposed solution (Fig. 5), it is clearly seen that the transition between the actively coded audio and the second comfort noise region is smoother for the latter. In this example a subset of the signal characteristics at the VAD
hangover frames are used to obtain the smooth transition. For other signals with shorter segments of active frames the parameter buffers might also con-tain parameters from close in time SID frames.
Although it is true that there will be only one first SID frame following an ac-tive signal frame, it will indirectly affect the CN parameters in following SID
frames due to the smoothing/interpolation.
Fig. 6 is a flow chart illustrating an example embodiment of the method in ac-cordance with the proposed technology. Step 51 stores CN parameters for SID
frames and active hangover frames in a buffer of a predetermined size. Step S2 determines a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on residual energies. Step S3 uses the deter-mined CN parameter subset to determine the CN control parameters for a first SID frame following an active signal frame (in other words, it determines the CN
control parameters for a first SID frame following an active signal frame based on the determined CN parameter subset).
Fig. 7 is a flow chart illustrating another example embodiment of the method in accordance with the proposed technology. The figure illustrates the method steps performed for each frame. Different parts of the buffer (such as 200 in Fig.
4) are updated depending on whether the frame is an active non-hangover frame or a SID/hangover frame (decided in step A, which corresponds to mode selector 26 in Fig. 4). If the frame is a SID or hangover frame, step la (corre-sponds to the unit that is denoted step la in Fig. 4) updates the buffer with new CN parameters, for example as described under subsection la above. If the frame is an active non-hangover frame, step lb (corresponds to the unit that is denoted step lb in Fig. 4) updates the size of an age restricted subset of the stored CN parameters based on the number of consecutive active non-hangover frames, for example as described under subsection lb above. Step 2 (corre-sponds to the unit that is denoted step 2 in Fig. 4) selects the CN parameter subset from the age restricted subset based on residual energies, for example as described under subsection 2 above. Step 3 (corresponds to the unit that is de-noted step 3 in Fig. 4) determines representative CN parameters from the CN
parameter subset, for example as described under subsection 3 above. Step 4 (corresponds to the unit that is denoted step 4 in Fig. 4) interpolates the repre-sentative CN parameters with decoded CN parameters, for example as described under subsection 4 above. Step B replaces the current frame with the next frame, and then the procedure is repeated with that frame.
Fig. 8 is a block diagram illustrating an example embodiment of the comfort noise controller 50 in accordance with the proposed technology. A buffer 200 of a predetermined size is configured to store CN parameters for SID frames and active hangover frames. A subset selector 50A is configured to determine a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on residual energies. A comfort noise control pa-rameter extractor 50B is configured to use the determined CN parameter subset to determine the CN control parameters for a first SID frame ("First SID") following an active signal frame.
Fig. 9 is a block diagram illustrating another example embodiment of the comfort noise controller 50 in accordance with the proposed technology. A
SID and hangover frame buffer updater 52 is configured to update, for SID
frames and active hangover frames, the buffer 200 with new CN parameters 4,E, for example as described under subsection la above. A non-hangover frame buffer updater 54 is configured to update, for active non-hangover frames, the size K of an age restricted subset QK ,EK of the stored CN pa-rameters based on the number p of consecutive active non-hangover frames, for example as described under subsection lb above. A buffer element selector 300 is configured to select the CN parameter subset QN,Es from the age restricted subset QK ,EK based on residual energies, for example as de-scribed under subsection 2 above. A comfort noise parameter estimator 400 is configured to determine representative CN parameters 14, E from the CN pa-rameter subset Q'E' , for example as described under subsection 3 above. A
comfort noise parameter interpolator 500 is configured to interpolate the representative CN parameters 14, E with decoded CN parameters a SID ,E SID for example as described under subsection 4 above. The obtained comfort noise control parameters q,E, for the first SID frame are then used by comfort noise generator 32 to control filling of no data frames with noise based on excitations from excitation generator 34.
The steps, functions, procedures and/or blocks described herein may be im-plemented in hardware using any conventional technology, such as discrete cir-cuit or integrated circuit technology, including both general-purpose electronic circuitry and application-specific circuitry.
Alternatively, at least some of the steps, functions, procedures and/or blocks described herein may be implemented in software for execution by suitable pro-cessing equipment. This equipment may include, for example, one or several micro processors, one or several Digital Signal Processors (DSP), one or several Application Specific Integrated Circuits (ASIC), video accelerated hardware or one or several suitable programmable logic devices, such as Field Programmable Gate Arrays (FPGA). Combinations of such processing elements are also feasi-ble.
It should also be understood that it may be possible to reuse the general pro-cessing capabilities already present in a network node, such as a mobile termi-nal or pc. This may, for example, be done by reprogramming of the existing software or by adding new software components.
Fig. 10 is a block diagram illustrating another example embodiment of a comfort 5 noise controller 50 in accordance with the proposed technology. This embodi-ment is based on a processor 62, for example a micro processor, which executes a computer program for generating CN control parameters. The program is stored in memory 64. The program includes a code unit 66 for storing CN pa-rameters for SID frames and active hangover frames in a buffer of predeter-1 0 mined size, a code unit 68 for determining a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and residual energies, and a code unit 70 for using the determined CN parameter subset to determine the CN control parameters for a first SID frame following an active signal frame.
The processor 62 communicates with the memory 64 over a system bus. The 15 inputs 10 a A 5 E, lir SID' E SID are received by an input/output (I/O) controller 72 con-trolling an I/O bus, to which the processor 62 and the memory 64 are connect-ed. The CN control parameters q, E, obtained from the program are outputted from the memory 64 by the I/O controller 72 over the I/O bus.
20 According to an aspect of the embodiments, a decoder for generating comfort noise representing an inactive signal is provided. The decoder can operate in DTX mode and can be implemented in a mobile terminal and by a computer program product which can be implemented in the mobile terminal or pc.
The computer program product can be downloaded from a server to the mo-2 5 bile terminal.
Figure 11 is a schematic diagram showing some components of an example embodiment of a decoder 100 wherein the functionality of the decoder is im-plemented by a computer. The computer comprises a processor 62 which is capable of executing software instructions contained in a computer program stored on a computer program product. Furthermore, the computer com-prises at least one computer program product in the form of a non-volatile memory 64 or volatile memory, e.g. an EEPROM (Electrically Erasable Pro-grammable Read-only Memory), a flash memory, a disk drive or a RAM (Ran-dom-access memory). The computer program, enables storing CN parame-ters for SID and active mode hangover frames in a buffer of a predetermined size, determining which of the stored CN parameters that are relevant for SID based on age of the stored CN parameters and residual energy meas-urements, and using the determined CN parameters that are relevant for SID
for estimating the CN parameters in the first SID frame following an active signal frame(s).
Fig. 12 is a block diagram illustrating a network node 80 that includes a com-fort noise controller 50 in accordance with the proposed technology. The net-work node 80 is typically a User Equipment (UE), such as a mobile terminal or PC. The comfort noise controller 50 may be provided in a decoder 100, as indi-1 5 cated by the dashed lines. As an alternative it may be provided in an encoder, as outlined above.
In the embodiments of the proposed technology described above the LP coef-ficients ak are transformed to an LSP domain. However, the same principles may also be applied to LP coefficients that are transformed to an LSF, ISP or ISF domain.
For codecs with attenuation of the comfort noise it can be beneficial to grad-ually attenuate the actively coded signal during VAD hangover frames. The energy for the comfort noise would then better match the latest actively cod-ed frame, which further improves the perceived audio quality. An attenua-tion factor A, can be computed and applied to the LP residual for each hang-over frame by:
s[n]= A = s[n]
(18) with i A, = max 0.6, _____________________________________________________________________ (19) 1+ 0.1/9,0 j where pHo is the number of consecutive VAD hangover frames. As an alter-native A, may be computed as:
)= max L, ___________________________________ (20) 1+ L ______________________________________________ PHo where L=0.6 and Lo =6 control the maximum attenuation and rate of at-tenuation. The maximum attenuation can typically be selected in the range L = [0.5,1) and the rate control parameter Lo for example be selected such that Lo = _______________________________________________________________ P 1-L HO , where pHFouLL is the number of frames needed for maxi-mum attenuation. pHFouLL could for example be set to the average or maximum number of consecutive VAD hangover frames that is possible (due to the hangover addition in the VAD). Typically this would be in the range of p FULL {i, 515} frames.
It should be understood that the technology described herein can co-operate with other solutions handling the first CN frames following active signal segments. For example, it can complement an algorithm where a large change in CN parameters is allowed for high energy frames (relative to back-ground noise level). For these frames the previous noise characteristics might not much affect the update in the current SID frame. The described technology may then be used for frames that are not detected as high energy frames.
It will be understood by those skilled in the art that various modifications and changes may be made to the proposed technology without departure from the scope thereof, which is defined by the appended claims.
ABBREVIATIONS
ACELP Algebraic Code-Excited Linear Prediction AMR Adaptive Multi-Rate AMR NB AMR Narrowband AR Auto Regressive ASIC Application Specific Integrated Circuits CN Comfort Noise DFT Discrete Fourier Transform DSP Digital Signal Processors DTX Discontinuous Transmission EEPROM Electrically Erasable Programmable Read-only Memory FPGA Field Programmable Gate Arrays ISF Immitance Spectrum Frequencies ISP Immitance Spectrum Pairs LP Linear Prediction7 LSF Line Spectral Frequencies LSP Line Spectral Pairs MDCT Modified Discrete Cosine Transform RAM Random-access memory SAD Sound Activity Detector SID Silence Insertion Descriptor UE User Equipment VAD Voice Activity Detector
Fig. 7 is a flow chart illustrating another example embodiment of the method in accordance with the proposed technology;
Fig. 8 is a block diagram illustrating an example embodiment of the com-fort noise controller in accordance with the proposed technology;
Fig. 9 is a block diagram illustrating another example embodiment of the comfort noise controller in accordance with the proposed technology;
Fig. 10 is a block diagram illustrating another example embodiment of the comfort noise controller in accordance with the proposed technology;
Fig. 11 is a schematic diagram showing some components of an example embodiment of a decoder, wherein the functionality of the decoder is imple-mented by a computer; and Fig. 12 is a block diagram illustrating a network node that includes a comfort noise controller in accordance with the proposed technology.
DETAILED DESCRIPTION
The embodiments described below relate to a system of audio encoder and decoder mainly intended for speech communication applications using DTX
with comfort noise for inactive signal representation. The system that is con-sidered utilizes LP for coding of both active and inactive signal frames, where a VAD is used for activity decisions.
In the encoder illustrated in Fig. 3 a VAD 18 outputs an activity decision which is used for the encoding by an encoder 20. In addition, the VAD hang-over decision is put into the bitstream by a bitstream multiplexer (MUX) 22 and transmitted to the decoder together with the coded parameters of active frames (hangover and non-hangover frames) and SID frames.
The disclosed embodiments are part of an audio decoder. Such a decoder 100 is schematically illustrated in figure 4. A bitstream demultiplexer (DEMUX) 24 demultiplexes the received bitstream into coded parameters and VAD hangover decisions. The demultiplexed signals are forwarded to a mode selector 26. Received coded parameters are decoded in a parameter decoder 28. The decoded parameters are used by an active frame decoder 30 to de-code active frames from the mode selector 26.
The decoder 100 also includes a buffer 200 of a predetermined size M and configured to receive and store CN parameters for SID and active mode hangover frames, a unit 300 configured to determine which of the stored CN
parameters that are relevant for SID based on the age of stored CN parame-1 0 ters, a unit 400 configured to determine which of the determined CN
param-eters that are relevant for SID based on residual energy measurements, and a unit 500 configured to use the determined CN parameters that are relevant for SID for the first SID frame following active signal frame(s).
The parameters in the buffers are constrained to be recent in order to be rel-evant. Thereby the sizes of the buffers used for selection of relevant buffer subsets are reduced during longer periods of active coding. Additionally the stored parameters are replaced by newer values during SID and actively cod-ed hangover frames.
By using circular buffers the complexity and memory requirement for the buffer handling can be reduced. In such implementation the already stored elements do not have to be moved when a new element is added. The posi-tion of the last added parameter, or parameter set, is used together with the size of the buffer to place new elements. When new elements are added, old elements might be overwritten.
Since the buffers hold parameters from earlier SID and hangover frames they describe signal characteristics of previous audio frames that probably, but not necessarily, contain background noise. The number of parameters that are considered relevant is defined by the size of the buffer and the time, or corresponding number of frames, elapsed since the information was stored.
The technology disclosed herein can be described in a number of algorithmic steps, e.g. performed at the decoder side illustrated in Fig. 4. These steps are:
la. Step la (performed by the unit denoted step la in Fig. 4) - Buffer update for SID and hangover frames:
For each SID and active hangover frame the quantized LSP coefficient vector 4 and corresponding quantized residual energy E are stored (in buffer 200) in buffers Qm = qmõ ,} and Em = Eom,...,Emm 1} , i.e.
qmj (7) Em =
The buffer position index j e [ 0,M -1] is increased by one prior to each buffer update and reset if the index exceeds the buffer size M, i.e.
j =0 f j > M -1 (8) As will be described below, subsets QK and EK of the K0 latest stored elements in QM and EM, respectively, define the sets of stored parame-ters.
lb. Step lb (performed by the unit denoted step lb in Fig. 4) - Buffer update for active non-hangover frames During decoding of active frames, the size of subsets QK and EK is de-creased by a rate of y' elements per frame according to:
{K = K
0 if pA<Y
(9) K = K -1 for 17 = y pA<(ri +1) = y where K0 is the number of stored elements in previous SID and hango-ver frames, ri cZ and PA is the number of consecutive active non-hangover frames. The rate of decrement relates to time, where y =25 is feasible for 20 ms frames. This corresponds to a decrease by one ele-ment every half second while decoding active frames. The decrement rate constant y can potentially be defined as any value y c Z , but it should be chosen such that old noise characteristics that are likely not to represent the current background noise are excluded from the sub-sets QK and EK . The value might for example be chosen based on the expected dynamics of the background noise. In addition, the natural length of speech bursts and the behavior of the VAD may be considered, as long sequences of consecutive active frames are unlikely. Typically the constant would be in the range y 500 for 20 ms frames, which cor-responds to less than 10 seconds. As an alternative equation (9) may be written in a more compact form as:
K = K0-17 for 17 = y pA <(17 +1) = y (10) where K0 is the number of CN parameters for SID frames and active hango-ver frames stored in the buffer 200, y is a predetermined constant, ri is a non-negative integer.
2. Step 2 (perfor _________________________________________________________________ tried by the unit denoted step 2 in Fig. 4) - Selection of relevant buffer elements At the first SID following active frames a subset of the buffer EK is se-lected based on the residual energies. The subset EN = { Eos ,...,E.. i } EK
of size L is defined as:
Es {EkK c EKE ri <EkK <EkKo +12}
for k =k0,...,kK 1 (11) where EkKo is the latest stored residual energy, yi and 12 are predetermined lower and upper bounds, respec-tively, for residual energies considered to be representative of noise at a transition from active to inactive frames (for example yi = 200 and y2 = 20 ), ko,...,k, I are sorted such that ko corresponds to the latest and ki, 1 to the oldest stored CN parameter.
Typically, 12 is selected from the range 12 c [0,100] as larger values would include high residual energies compared to the latest stored residual energy EkKo . This could cause a significant step-up of the comfort noise energy that would cause an audible degradation. It is also desirable to exclude signal characteristics from speech frames, which generally have larger energy, as these characteristics are generally not representing the background noise well. 11 can be selected slightly larger than 12' e.g.
from the range 11 c [50,500], as a step-down in energy is usually less an-noying. Additionally, the likelihood of including speech signal character-istics is generally less for frames with a residual energy less than EkKo than it is for frames with a residual energy larger than EkKo .
It should be noted that the energies EkK can as well as in linear domain be represented in a logarithmic domain, e.g. dB. With energies in loga-rithmic domain the selection of relevant buffer elements, as specified in equation (11), is described equivalently with energies EkK in linear do-main as:
Es = EkK c EKE <EK < EkKo 172. } for k 1 (12) where log(Ti )= -yi and 1og(1-2) = 12. Suitable boundaries specifying the subset of the buffer EK are for example given by Ti = 0.7 and 22 = 1.03 or c [0.5,0.9] and 12 E [1.0,1.25] .
The corresponding vectors in the LSP buffer QK define the subset Qs-rs 3. Step 3 (performed by the unit denoted step 3 in Fig. 4) - Determination of representative comfort noise parameters To find a representative residual energy the weighted mean of the sub-set Es is computed as:
Ewks Eks E _ ______________________________________________________________________________ (13) k=0 where w: are the elements in the subset of weights:
s wm E wm w = for VjlE7 e For a maximum buffer size AI = 8 a suitable set of weights is:
wm = {0.2, 0.16, 0.128, 0.1024, 0.08192, 0.065536, 0.0524288, 0.01048576}
This means that recent energies get more weight in the residual energy mean E, which makes the energy transition between active and inactive frames smoother.
Among LSP vectors in the subset QS, the median LSP vector is selected by computing the distances between all the LSP vectors in the subset buffer Es according to:
P
Rim E(q, is [p] qms [42 for l,m=0,...,L -1 ( 14) P=1 where q[p] are the elements in the vector qis .
For every LSP vector the distance to the other vectors are summed, i.e.
Si = E Rim for l =0,...,L -1 (15) m=0 The median LSP vector is given by the vector with the smallest distance to the other vectors in the subset buffer, i.e.
el = { cli E Qs 1 si sõõ t # m } for l,m =0,...,L -1 (16) If several vectors have equal total distance, the median can be arbitrari-ly chosen among those vectors.
As an alternative representative LSP vector may be determined as the mean vector of the subset Qs .
4. Step 4 (performed by the unit denoted step 4 in Fig. 4) - Interpolation of comfort noise parameters for first SID frame The LSP median or mean vector 44 and the averaged residual energy E
are used in the interpolation of CN parameters in the first SID frame as described in equation (5) and (6) with:
(17) E1-1 = E
The values of iva .SID and E's, are obtained from the parameter decoder 28.
The smoothing factors a e [0,1] and p c [0,1] can for the first SID frame be different from the factors used in following SID and no data frames interpolation of CN parameters. Additionally, the factors could for ex-ample be dependent on a measure that further describe the reliability of the determined parameters el and E, e.g. the size of the subsets Qs and Es. Suitable values are for example a =0.2 and f3 = 0.2 or f3 = 0.05.
The comfort noise parameters for the first SID frame are then used by a comfort noise generator 32 to control filling of no data frames from mode selector 26 with noise based on excitations from excitation gener-ator 34.
If the subsets Qs and Es are empty, the latest extracted SID parameters may be used directly without interpolation from older noise parameters.
The transmitted LSP vector .SID used in the interpolation is in the encoder usually obtained directly from the LP analysis of the current frame, i.e. no previous frames are considered. The transmitted residual energy r SID is pref-erably obtained using LP parameters corresponding to the LSP parameters used for the signal synthesis in the decoder. These LSP parameters can be obtained in the encoder by performing steps 1-4 with a corresponding en-coder side buffer. Operating the encoder in this way implies that the energy of the decoder output can be matched to the input signal energy by control of the encoded and transmitted residual energy since the decoder synthesis LP parameters are known in the encoder.
Fig. 5 is an example of a spectrogram of a noisy speech signal that has been de-coded in accordance with the proposed technology. The spectrogram corre-sponds to the spectrogram in Fig. 2, i.e. it is based on the same encoder side input signal. By comparing the spectrograms of the prior art (Fig. 2) and the proposed solution (Fig. 5), it is clearly seen that the transition between the actively coded audio and the second comfort noise region is smoother for the latter. In this example a subset of the signal characteristics at the VAD
hangover frames are used to obtain the smooth transition. For other signals with shorter segments of active frames the parameter buffers might also con-tain parameters from close in time SID frames.
Although it is true that there will be only one first SID frame following an ac-tive signal frame, it will indirectly affect the CN parameters in following SID
frames due to the smoothing/interpolation.
Fig. 6 is a flow chart illustrating an example embodiment of the method in ac-cordance with the proposed technology. Step 51 stores CN parameters for SID
frames and active hangover frames in a buffer of a predetermined size. Step S2 determines a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on residual energies. Step S3 uses the deter-mined CN parameter subset to determine the CN control parameters for a first SID frame following an active signal frame (in other words, it determines the CN
control parameters for a first SID frame following an active signal frame based on the determined CN parameter subset).
Fig. 7 is a flow chart illustrating another example embodiment of the method in accordance with the proposed technology. The figure illustrates the method steps performed for each frame. Different parts of the buffer (such as 200 in Fig.
4) are updated depending on whether the frame is an active non-hangover frame or a SID/hangover frame (decided in step A, which corresponds to mode selector 26 in Fig. 4). If the frame is a SID or hangover frame, step la (corre-sponds to the unit that is denoted step la in Fig. 4) updates the buffer with new CN parameters, for example as described under subsection la above. If the frame is an active non-hangover frame, step lb (corresponds to the unit that is denoted step lb in Fig. 4) updates the size of an age restricted subset of the stored CN parameters based on the number of consecutive active non-hangover frames, for example as described under subsection lb above. Step 2 (corre-sponds to the unit that is denoted step 2 in Fig. 4) selects the CN parameter subset from the age restricted subset based on residual energies, for example as described under subsection 2 above. Step 3 (corresponds to the unit that is de-noted step 3 in Fig. 4) determines representative CN parameters from the CN
parameter subset, for example as described under subsection 3 above. Step 4 (corresponds to the unit that is denoted step 4 in Fig. 4) interpolates the repre-sentative CN parameters with decoded CN parameters, for example as described under subsection 4 above. Step B replaces the current frame with the next frame, and then the procedure is repeated with that frame.
Fig. 8 is a block diagram illustrating an example embodiment of the comfort noise controller 50 in accordance with the proposed technology. A buffer 200 of a predetermined size is configured to store CN parameters for SID frames and active hangover frames. A subset selector 50A is configured to determine a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and on residual energies. A comfort noise control pa-rameter extractor 50B is configured to use the determined CN parameter subset to determine the CN control parameters for a first SID frame ("First SID") following an active signal frame.
Fig. 9 is a block diagram illustrating another example embodiment of the comfort noise controller 50 in accordance with the proposed technology. A
SID and hangover frame buffer updater 52 is configured to update, for SID
frames and active hangover frames, the buffer 200 with new CN parameters 4,E, for example as described under subsection la above. A non-hangover frame buffer updater 54 is configured to update, for active non-hangover frames, the size K of an age restricted subset QK ,EK of the stored CN pa-rameters based on the number p of consecutive active non-hangover frames, for example as described under subsection lb above. A buffer element selector 300 is configured to select the CN parameter subset QN,Es from the age restricted subset QK ,EK based on residual energies, for example as de-scribed under subsection 2 above. A comfort noise parameter estimator 400 is configured to determine representative CN parameters 14, E from the CN pa-rameter subset Q'E' , for example as described under subsection 3 above. A
comfort noise parameter interpolator 500 is configured to interpolate the representative CN parameters 14, E with decoded CN parameters a SID ,E SID for example as described under subsection 4 above. The obtained comfort noise control parameters q,E, for the first SID frame are then used by comfort noise generator 32 to control filling of no data frames with noise based on excitations from excitation generator 34.
The steps, functions, procedures and/or blocks described herein may be im-plemented in hardware using any conventional technology, such as discrete cir-cuit or integrated circuit technology, including both general-purpose electronic circuitry and application-specific circuitry.
Alternatively, at least some of the steps, functions, procedures and/or blocks described herein may be implemented in software for execution by suitable pro-cessing equipment. This equipment may include, for example, one or several micro processors, one or several Digital Signal Processors (DSP), one or several Application Specific Integrated Circuits (ASIC), video accelerated hardware or one or several suitable programmable logic devices, such as Field Programmable Gate Arrays (FPGA). Combinations of such processing elements are also feasi-ble.
It should also be understood that it may be possible to reuse the general pro-cessing capabilities already present in a network node, such as a mobile termi-nal or pc. This may, for example, be done by reprogramming of the existing software or by adding new software components.
Fig. 10 is a block diagram illustrating another example embodiment of a comfort 5 noise controller 50 in accordance with the proposed technology. This embodi-ment is based on a processor 62, for example a micro processor, which executes a computer program for generating CN control parameters. The program is stored in memory 64. The program includes a code unit 66 for storing CN pa-rameters for SID frames and active hangover frames in a buffer of predeter-1 0 mined size, a code unit 68 for determining a CN parameter subset relevant for SID frames based on the age of the stored CN parameters and residual energies, and a code unit 70 for using the determined CN parameter subset to determine the CN control parameters for a first SID frame following an active signal frame.
The processor 62 communicates with the memory 64 over a system bus. The 15 inputs 10 a A 5 E, lir SID' E SID are received by an input/output (I/O) controller 72 con-trolling an I/O bus, to which the processor 62 and the memory 64 are connect-ed. The CN control parameters q, E, obtained from the program are outputted from the memory 64 by the I/O controller 72 over the I/O bus.
20 According to an aspect of the embodiments, a decoder for generating comfort noise representing an inactive signal is provided. The decoder can operate in DTX mode and can be implemented in a mobile terminal and by a computer program product which can be implemented in the mobile terminal or pc.
The computer program product can be downloaded from a server to the mo-2 5 bile terminal.
Figure 11 is a schematic diagram showing some components of an example embodiment of a decoder 100 wherein the functionality of the decoder is im-plemented by a computer. The computer comprises a processor 62 which is capable of executing software instructions contained in a computer program stored on a computer program product. Furthermore, the computer com-prises at least one computer program product in the form of a non-volatile memory 64 or volatile memory, e.g. an EEPROM (Electrically Erasable Pro-grammable Read-only Memory), a flash memory, a disk drive or a RAM (Ran-dom-access memory). The computer program, enables storing CN parame-ters for SID and active mode hangover frames in a buffer of a predetermined size, determining which of the stored CN parameters that are relevant for SID based on age of the stored CN parameters and residual energy meas-urements, and using the determined CN parameters that are relevant for SID
for estimating the CN parameters in the first SID frame following an active signal frame(s).
Fig. 12 is a block diagram illustrating a network node 80 that includes a com-fort noise controller 50 in accordance with the proposed technology. The net-work node 80 is typically a User Equipment (UE), such as a mobile terminal or PC. The comfort noise controller 50 may be provided in a decoder 100, as indi-1 5 cated by the dashed lines. As an alternative it may be provided in an encoder, as outlined above.
In the embodiments of the proposed technology described above the LP coef-ficients ak are transformed to an LSP domain. However, the same principles may also be applied to LP coefficients that are transformed to an LSF, ISP or ISF domain.
For codecs with attenuation of the comfort noise it can be beneficial to grad-ually attenuate the actively coded signal during VAD hangover frames. The energy for the comfort noise would then better match the latest actively cod-ed frame, which further improves the perceived audio quality. An attenua-tion factor A, can be computed and applied to the LP residual for each hang-over frame by:
s[n]= A = s[n]
(18) with i A, = max 0.6, _____________________________________________________________________ (19) 1+ 0.1/9,0 j where pHo is the number of consecutive VAD hangover frames. As an alter-native A, may be computed as:
)= max L, ___________________________________ (20) 1+ L ______________________________________________ PHo where L=0.6 and Lo =6 control the maximum attenuation and rate of at-tenuation. The maximum attenuation can typically be selected in the range L = [0.5,1) and the rate control parameter Lo for example be selected such that Lo = _______________________________________________________________ P 1-L HO , where pHFouLL is the number of frames needed for maxi-mum attenuation. pHFouLL could for example be set to the average or maximum number of consecutive VAD hangover frames that is possible (due to the hangover addition in the VAD). Typically this would be in the range of p FULL {i, 515} frames.
It should be understood that the technology described herein can co-operate with other solutions handling the first CN frames following active signal segments. For example, it can complement an algorithm where a large change in CN parameters is allowed for high energy frames (relative to back-ground noise level). For these frames the previous noise characteristics might not much affect the update in the current SID frame. The described technology may then be used for frames that are not detected as high energy frames.
It will be understood by those skilled in the art that various modifications and changes may be made to the proposed technology without departure from the scope thereof, which is defined by the appended claims.
ABBREVIATIONS
ACELP Algebraic Code-Excited Linear Prediction AMR Adaptive Multi-Rate AMR NB AMR Narrowband AR Auto Regressive ASIC Application Specific Integrated Circuits CN Comfort Noise DFT Discrete Fourier Transform DSP Digital Signal Processors DTX Discontinuous Transmission EEPROM Electrically Erasable Programmable Read-only Memory FPGA Field Programmable Gate Arrays ISF Immitance Spectrum Frequencies ISP Immitance Spectrum Pairs LP Linear Prediction7 LSF Line Spectral Frequencies LSP Line Spectral Pairs MDCT Modified Discrete Cosine Transform RAM Random-access memory SAD Sound Activity Detector SID Silence Insertion Descriptor UE User Equipment VAD Voice Activity Detector
Claims (17)
1. A method of generating Comfort Noise, CN, control parameters, characterized by:
storing (S1; 1a) CN parameters (q~, E~) for Silence Insertion De-scriptor, SID, frames and active hangover frames in a buffer (200) of a prede-termined size (M);
determining (S2, 1b, 2) a CN parameter subset (Q S,E S) relevant for SID frames based on the age of the stored CN parameters and on residual energies;
using (S3, 3, 4) the determined CN parameter subset (Q S,E S) to de-termine the CN control parameters (q i, E i) for a first SID frame ("First SID") following an active signal frame.
storing (S1; 1a) CN parameters (q~, E~) for Silence Insertion De-scriptor, SID, frames and active hangover frames in a buffer (200) of a prede-termined size (M);
determining (S2, 1b, 2) a CN parameter subset (Q S,E S) relevant for SID frames based on the age of the stored CN parameters and on residual energies;
using (S3, 3, 4) the determined CN parameter subset (Q S,E S) to de-termine the CN control parameters (q i, E i) for a first SID frame ("First SID") following an active signal frame.
2. The method of claim 1, characterized by:
updating (1a), for SID frames and active hangover frames, the buffer (200) with new CN parameters (~,~);
updating (1b), for active non-hangover frames, the size K of an age restricted subset (Q K,E K ) of the stored CN parameters based on the number p A of consecutive active non-hangover frames;
selecting (2) the CN parameter subset (Q S, E S) from the age restricted subset (Q K,E K ) based on residual energies;
determining (3) representative CN parameters (~,~) from the CN pa-rameter subset (Q S, E S);
interpolating the representative CN parameters (~,~) with decoded CN
parameters (~ SID ,~ SID).
updating (1a), for SID frames and active hangover frames, the buffer (200) with new CN parameters (~,~);
updating (1b), for active non-hangover frames, the size K of an age restricted subset (Q K,E K ) of the stored CN parameters based on the number p A of consecutive active non-hangover frames;
selecting (2) the CN parameter subset (Q S, E S) from the age restricted subset (Q K,E K ) based on residual energies;
determining (3) representative CN parameters (~,~) from the CN pa-rameter subset (Q S, E S);
interpolating the representative CN parameters (~,~) with decoded CN
parameters (~ SID ,~ SID).
3. The method of claim 2, characterized by updating (1b), for active non-hangover frames, the size K of the age restricted subset (Q K,E K) in accord-ance with:
K =K0-.eta. for .eta. .cndot..gamma. <= P A <(.eta. +1).cndot..gamma.
where K0 is the number of CN parameters for SID frames and active hango-ver frames stored in the buffer (200), .gamma. is a predetermined constant, .eta. is a non-negative integer.
K =K0-.eta. for .eta. .cndot..gamma. <= P A <(.eta. +1).cndot..gamma.
where K0 is the number of CN parameters for SID frames and active hango-ver frames stored in the buffer (200), .gamma. is a predetermined constant, .eta. is a non-negative integer.
4. The method of claim 2 or 3, characterized by selecting (2) the CN parame-ter subset (Q N,E S) from the age restricted subset (Q K,E K) by including only CN parameters for which:
E~ - .gamma.1 < E~ < E~ + .gamma.2 for k = k0,.multidot.,k K-1 where E~ is the latest stored residual energy, .gamma.1 and .gamma.2 are predetermined lower and upper bounds, respectively, for residual energies considered to be representative of noise at a transition from active to inactive frames, k0,...,k K-1 are sorted such that k0 corresponds to the latest and k K-1 to the oldest stored CN parameter.
E~ - .gamma.1 < E~ < E~ + .gamma.2 for k = k0,.multidot.,k K-1 where E~ is the latest stored residual energy, .gamma.1 and .gamma.2 are predetermined lower and upper bounds, respectively, for residual energies considered to be representative of noise at a transition from active to inactive frames, k0,...,k K-1 are sorted such that k0 corresponds to the latest and k K-1 to the oldest stored CN parameter.
5. The method of claim 2, 3 or 4, characterized by determining (3) repre-sentative CN parameters ~, ~ from the CN parameter subset (Q S,E S, where ~ is the median vector of a set Q S of vectors in the CN parameter subset (Q S, E S) representing Auto Regressive, AR, coefficients, and ~ is a weighted mean residual energy of a set E s of residual energies in the selected CN parameter subset (Q S, E S).
6. The method of claim 5, characterized in that the median vector ~ repre-sents the AR coefficients as Line Spectral Pairs.
7. A computer program for generating Comfort Noise, CN, control parameters, comprising computer readable code units which when run on a computer (60) causes the computer to:
store (66; S1; 1a) CN parameters (q~, E~) for Silence Insertion De-scriptor, SID, frames and active hangover frames in a buffer (200) of a prede-termined size (M);
determine (68; S2; 1b, 2) a CN parameter subset (Q S,E S) relevant for SID frames based on the age of the stored CN parameters and on residual energies;
use (68; S3; 3, 4) the determined CN parameter subset (Q S, E S) to de-termine the CN control parameters (q i,E i) for a first SID frame ("First SID") following an active signal frame.
store (66; S1; 1a) CN parameters (q~, E~) for Silence Insertion De-scriptor, SID, frames and active hangover frames in a buffer (200) of a prede-termined size (M);
determine (68; S2; 1b, 2) a CN parameter subset (Q S,E S) relevant for SID frames based on the age of the stored CN parameters and on residual energies;
use (68; S3; 3, 4) the determined CN parameter subset (Q S, E S) to de-termine the CN control parameters (q i,E i) for a first SID frame ("First SID") following an active signal frame.
8. A computer program product, comprising computer readable medium and a computer program according to claim 7 stored on the computer readable medium.
9. A comfort noise controller (50) for generating Comfort Noise, CN, control pa-rameters, characterized by:
a buffer (200) of a predetermined size (M) configured to store CN pa-rameters (q~ ,E~) for SID frames and active hangover frames;
a subset selector (50A; 54, 300) configured to determine a CN parame-ter subset (Q S, E S) relevant for Silence Insertion Descriptor, SID, frames based on the age of the stored CN parameters and on residual energies;
a comfort noise control parameter extractor (50B; 400, 500) configured to use the determined CN parameter subset (Q S, E S) to determine the CN
control parameters (q,E,) for a first SID frame ("First SID") following an ac-tive signal frame.
a buffer (200) of a predetermined size (M) configured to store CN pa-rameters (q~ ,E~) for SID frames and active hangover frames;
a subset selector (50A; 54, 300) configured to determine a CN parame-ter subset (Q S, E S) relevant for Silence Insertion Descriptor, SID, frames based on the age of the stored CN parameters and on residual energies;
a comfort noise control parameter extractor (50B; 400, 500) configured to use the determined CN parameter subset (Q S, E S) to determine the CN
control parameters (q,E,) for a first SID frame ("First SID") following an ac-tive signal frame.
10. The controller (50) of claim 9, characterized by:
a SID and hangover frame buffer updater (52) configured to update, for SID frames and active hangover frames, the buffer (200) with new CN parame-ters (~,~);
a non-hangover frame buffer updater (54) configured to update, for active non-hangover frames, the size K of an age restricted subset (Q K,E K) of the stored CN parameters based on the number P A of consecutive active non-hangover frames;
a buffer element selector (300) configured to select the CN parameter subset (Q S,E S) from the age restricted subset (Q K,E K) based on residual en-ergies;
a comfort noise parameter estimator (400) configured to determine (3) representative CN parameters (~,~) from the CN parameter subset (Q S,E S;
a comfort noise parameter interpolator (500) configured to interpolate the representative CN parameters (~,~) with decoded CN parameters (~ SID, ~ SID) .
a SID and hangover frame buffer updater (52) configured to update, for SID frames and active hangover frames, the buffer (200) with new CN parame-ters (~,~);
a non-hangover frame buffer updater (54) configured to update, for active non-hangover frames, the size K of an age restricted subset (Q K,E K) of the stored CN parameters based on the number P A of consecutive active non-hangover frames;
a buffer element selector (300) configured to select the CN parameter subset (Q S,E S) from the age restricted subset (Q K,E K) based on residual en-ergies;
a comfort noise parameter estimator (400) configured to determine (3) representative CN parameters (~,~) from the CN parameter subset (Q S,E S;
a comfort noise parameter interpolator (500) configured to interpolate the representative CN parameters (~,~) with decoded CN parameters (~ SID, ~ SID) .
11. The controller (50) of claim 10, characterized in that the buffer element selector (300) is configured to update, for active non-hangover frames, the size K of the age restricted subset (Q K,E K) in accordance with:
K = K0-.eta. for .eta. .cndot. .gamma. <= P A(.eta.+ 1) .cndot. .gamma.
where K0 is the number of CN parameters for SID frames and active hango-ver frames stored in the buffer (200), .gamma. is a predetermined constant, .eta. is a non-negative integer.
K = K0-.eta. for .eta. .cndot. .gamma. <= P A(.eta.+ 1) .cndot. .gamma.
where K0 is the number of CN parameters for SID frames and active hango-ver frames stored in the buffer (200), .gamma. is a predetermined constant, .eta. is a non-negative integer.
12. The controller (50) of claim 10 or 11, characterized in that the buffer el-ement selector (300) is configured to select the CN parameter subset (Q s,E s) from the age restricted subset (Q K,E K) by including only CN parameters for which:
, for k = k0,...,k K-1 where E~ is the latest stored residual energy, .gamma.1 and .gamma.2 are predetermined lower and upper bounds, respectively, for residual energies considered to be representative of noise at a transition from active to inactive frames, k0,...,k K-1 are sorted such that ko corresponds to the latest and k K-1 to the oldest stored CN parameter.
, for k = k0,...,k K-1 where E~ is the latest stored residual energy, .gamma.1 and .gamma.2 are predetermined lower and upper bounds, respectively, for residual energies considered to be representative of noise at a transition from active to inactive frames, k0,...,k K-1 are sorted such that ko corresponds to the latest and k K-1 to the oldest stored CN parameter.
13. The controller (50) of claim 10, 11 or 12, characterized in that the com-fort noise parameter estimator (400) is configured to determine representa-tive CN parameters ~,~ from the CN parameter subset (Q s,E s), where ~ is the median vector of a set Q s of vectors in the CN parameter subset (Q s,E s) representing Auto Regressive, AR, coefficients, and ~ is a weighted mean residual energy of a set E s of residual energies in the selected CN parameter subset (Q s,E s).
14. A decoder (100) including a comfort noise controller (50) in accordance with any of the preceding claims 9-13.
15. A network node (80) including a decoder (100) in accordance with claim 14.
16. A network node (80) including a comfort noise controller (50) in accord-ance with any of the preceding claims 9-13.
17. The network node (80) of any of the preceding claims 14-16, wherein the network node is a mobile terminal.
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